paper
2022
Post-processing & calibration
Local & station forecasting New model or method
Classical ML (trees, GP, linear, kernels)6 h–3 daysKorea Meteorological Administration (KMA) Journal
2022-09-30
022
Sep 2022
Bu-Yo Kim, Miloslav Belorid, Joo Wan Cha
Weather and Forecasting
Korea Meteorological Administration (KMA)
Tree-based XGBoost trained on LDAPS NWP output beats raw LDAPS for Seoul visibility (MAE 2.04 km vs 4.66 km, R 0.88 vs 0.40).
Short-Term Visibility Prediction Using Tree-Based Machine Learning Algorithms and Numerical Weather Prediction Data
In one sentence. Tree-based XGBoost trained on LDAPS NWP output beats raw LDAPS for Seoul visibility (MAE 2.04 km vs 4.66 km, R 0.88 vs 0.40).
Labels
- Topic
- Post-processing & calibration, Local & station forecasting
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- 6 h–3 days
- Domain
- Local / station
- Data
- NWP analysis or forecasts (IFS, GFS, ...), Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.1175/waf-d-22-0053.1 OpenAlex:W4298109833
- Added
- 2026-10-22
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